Inferring cosmic string tension through the neural network prediction of string locations in CMB maps

Abstract

In previous work, we constructed a convolutional neural network used to estimate the location of cosmic strings in simulated cosmic microwave background temperature anisotropy maps. We derived a connection between the estimates of cosmic string locations by this neural network and the posterior probability distribution of the cosmic string tension $G\mu$. Here, we significantly improve the calculation of the posterior distribution of the string tension $G\mu$. We also improve our previous plain convolutional neural network by using residual networks. We apply our new neural network and posterior calculation method to maps from the same simulation used in our previous work and quantify the improvement.